Pixelwise template matching may be used to perform exhaustive searching of an entire image to find pixels similar to a query pixel. In patients undergoing treatment for prostate cancer (CaP) it is useful to distinguish cancerous tissue regions from benign tissue regions in magnetic resonance (MR) images on a pixel-by-pixel basis. A challenge to overcome when matching a pair of pixels is a trade-off between matching speed and accuracy. For example, a higher matching speed may be achieved at the cost of lower accuracy. Local Binary Pattern (LBP) pixelwise template matching is one conventional method of matching a local pixel feature to another pixel. Timo Ojala et al., Multiresolution gray-scale and rotation invariant texture classification with local binary patterns, IEEE TPAMI, vol. 24, pp. 971-987, 2002. The LBP descriptor of a pixel is a collection (e.g., string) of binary bits obtained by comparing the gray value of the pixel with the gray value of other pixels sampled within a ring of a certain radius centered on the pixel. The Hamming distance of LBP refers to the number of bits that are different. Computing the Hamming distance involves carrying out bitwise XOR operations that a computer may compute relatively quickly. Finding a distinctive ring radius facilitates extracting salient LBP descriptions.
Conventional LBP methods detect local Laplacian extrema. Lowe, Distinctive image features from scale-invariant keypoints, IJCV, vol. 60, pp. 91-110, 2004. Bay et al., Speeded-up robust features (surf), CVIU, vol. 110, pp. 346-359, 2008. However, conventional methods are computationally costly, which substantially negates the benefits of LBP matching. In conventional methods, multiple radii may be sampled to guarantee measuring textual content a salient scale. By assuming independent sampling, measuring multi-scale LBP (MsLBP) is defined as the sum of the Hamming scores across individual scales. While conventional MsLBP is an improvement over basic LBP matching, conventional MsLBP under-emphasizes salient patterns while over-emphasizing insignificant patterns. In conventional MsLBP, a weight vector must be defined to account for the statistical significance of information at the salient scale by measuring the dissimilarity between a pair of multi-scale LBPs.
Learned binary projections facilitate indexing large image collections based on content. Strecha et al., Ldahash: Improved matching with smaller descriptors, IEEE TPAMI, vol. 34, pp. 66-78, 2012. However, conventional unsupervised hashing leads to binary codes that may offer no improvement over random binarization. Pauleve et al., Locality sensitive hashing: A comparison of hash function types and querying mechanisms, PRL, vol. 31, pp. 1348-1358, 2010. Weiss et al., Spectral hashing, in NIPS, 2008, pp. 1753-1760. With supervised learning imposed, supervised hashing explicitly learns a mapping that maximizes the distances among different classes. However, conventional supervised hashing suffers from a non-differentiable sign function. The non-differentiable sign function forces a relaxation of the objective function, which results in a sub-optimal solution.
Conventional methods of pixelwise template matching thus suffer from high computation costs related to detecting Laplacian extrema that largely negate the benefits of LBP. Conventional methods of MsLBP also under-emphasize salient patterns while over-emphasizing insignificant patterns. Unsupervised hashing methods can lead to results no better than random binarization, while conventional supervised hashing is forced to relax the objective function due to the non-differentiable sign function. Conventional methods of pixelwise template matching thus suffer from high computation costs and inaccuracies that render them less than optimal for assessing MR images of a section of tissue taken from a CaP patient.